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            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
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          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
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    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "##### Copyright 2023 Google LLC. SPDX-License-Identifier: Apache-2.0"
      ],
      "metadata": {
        "id": "u2E0aERL8YpW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Copyright 2023 Google LLC. SPDX-License-Identifier: Apache-2.0\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n",
        "\n",
        "https://www.apache.org/licenses/LICENSE-2.0\n",
        "\n",
        "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
      ],
      "metadata": {
        "id": "xgKBf6Ot76Y5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners** Tabletop Rearrangement\n",
        "\n",
        "[KnowNo](https://robot-help.github.io) is a framework for measuring and aligning the uncertainty of LLM-based planners, such that they know when they don't know, and ask for help when needed. KnowNo builds on the theory of conformal prediction to provide statistical guarantees on task completion while minimizing human help.\n",
        "\n",
        "This colab runs an example of KnowNo for tabletop rearrangement in simulation (first experiment setting in the paper):\n",
        "\n",
        "\u003cimg src=\"https://robot-help.github.io/img/robot-help-teaser.png\" height=\"280px\"\u003e\n",
        "\n",
        "Note:\n",
        "* We use [GPT-3.5](https://arxiv.org/abs/2005.14165) (text-davinci-003) as the language model here.\n",
        "* For convenience, we will assume known locations of the objects on the table. Alternatively, one can use open-vocab object detectors such as [Owl-ViT](https://arxiv.org/abs/2205.06230) for perception as done in the paper.\n",
        "* The calibration invoves running LLM inference for calibration data. We recommend 200 data for good calibration results. You can also skip the calibration and directly test in new scenarios at the last block based on provided calibration result, or check out this colab.\n",
        "\n",
        "This colab is modified from the Socratic Models - Robot Perception \u0026 Planning [colab](https://colab.research.google.com/drive/1jAyhumd7DTxJB2oZufob9crVxETAEKbV?usp=sharing)."
      ],
      "metadata": {
        "id": "o29RKDf680GZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "openai_api_key = \"your-api-key\""
      ],
      "metadata": {
        "id": "PZdNE66A-CrF"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Setup**\n",
        "\n"
      ],
      "metadata": {
        "id": "d2UdCCcpwbl0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown This installs a few things like PyBullet (simulator) and MoviePy (videos), imports a few Python packages, and downloads PyBullet assets. Then we set up a simple tabletop pick-and-place environment in PyBullet.\n",
        "\n",
        "!pip install ftfy regex tqdm fvcore imageio==2.4.1 imageio-ffmpeg==0.4.5\n",
        "!pip install -U --no-cache-dir gdown --pre\n",
        "!pip install pybullet moviepy\n",
        "!pip install openai\n",
        "!pip install easydict\n",
        "\n",
        "import collections\n",
        "import datetime\n",
        "import os\n",
        "import random\n",
        "import threading\n",
        "import time\n",
        "\n",
        "import cv2  # Used by ViLD.\n",
        "from easydict import EasyDict\n",
        "import imageio\n",
        "import IPython\n",
        "import matplotlib.pyplot as plt\n",
        "from moviepy.editor import ImageSequenceClip\n",
        "import numpy as np\n",
        "import openai\n",
        "import pickle\n",
        "from PIL import Image\n",
        "import pybullet\n",
        "import pybullet_data\n",
        "import tqdm.notebook as tqdm\n",
        "\n",
        "#Download PyBullet assets.\n",
        "if not os.path.exists('ur5e/ur5e.urdf'):\n",
        "  !gdown --id 1Cc_fDSBL6QiDvNT4dpfAEbhbALSVoWcc\n",
        "  !gdown --id 1yOMEm-Zp_DL3nItG9RozPeJAmeOldekX\n",
        "  !gdown --id 1GsqNLhEl9dd4Mc3BM0dX3MibOI1FVWNM\n",
        "  !unzip ur5e.zip\n",
        "  !unzip robotiq_2f_85.zip\n",
        "  !unzip bowl.zip\n",
        "\n",
        "# Set OpenAI API key.\n",
        "openai.api_key = openai_api_key"
      ],
      "metadata": {
        "id": "NBSzsHw89qMp",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown **LLM API call:** completion and scoring optionally\n",
        "import signal\n",
        "class timeout:\n",
        "    def __init__(self, seconds=1, error_message='Timeout'):\n",
        "        self.seconds = seconds\n",
        "        self.error_message = error_message\n",
        "\n",
        "    def handle_timeout(self, signum, frame):\n",
        "        raise TimeoutError(self.error_message)\n",
        "\n",
        "    def __enter__(self):\n",
        "        signal.signal(signal.SIGALRM, self.handle_timeout)\n",
        "        signal.alarm(self.seconds)\n",
        "\n",
        "    def __exit__(self, type, value, traceback):\n",
        "        signal.alarm(0)\n",
        "\n",
        "# OpenAI only supports up to five tokens (logprobs argument) for getting the likelihood.\n",
        "# Thus we use the logit_bias argument to force LLM only consdering the five option\n",
        "# tokens: A, B, C, D, E\n",
        "def lm(prompt,\n",
        "       max_tokens=256,\n",
        "       temperature=0,\n",
        "       logprobs=None,\n",
        "       stop_seq=None,\n",
        "       logit_bias={\n",
        "          317: 100.0,   #  A (with space at front)\n",
        "          347: 100.0,   #  B (with space at front)\n",
        "          327: 100.0,   #  C (with space at front)\n",
        "          360: 100.0,   #  D (with space at front)\n",
        "          412: 100.0,   #  E (with space at front)\n",
        "      },\n",
        "       timeout_seconds=20):\n",
        "  max_attempts = 5\n",
        "  for _ in range(max_attempts):\n",
        "      try:\n",
        "          with timeout(seconds=timeout_seconds):\n",
        "              response = openai.Completion.create(\n",
        "                  model='text-davinci-003',\n",
        "                  prompt=prompt,\n",
        "                  max_tokens=max_tokens,\n",
        "                  temperature=temperature,\n",
        "                  logprobs=logprobs,\n",
        "                  logit_bias=logit_bias,\n",
        "                  stop=list(stop_seq) if stop_seq is not None else None,\n",
        "              )\n",
        "          break\n",
        "      except:\n",
        "          print('Timeout, retrying...')\n",
        "          pass\n",
        "  return response, response[\"choices\"][0][\"text\"].strip()"
      ],
      "metadata": {
        "cellView": "form",
        "id": "mETsjucUwkIh"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown **Global constants:** pick and place objects, colors, workspace bounds.\n",
        "\n",
        "PICK_TARGETS = {\n",
        "  'blue block': None,\n",
        "  'red block': None,\n",
        "  'green block': None,\n",
        "  'orange block': None,\n",
        "  'yellow block': None,\n",
        "  'purple block': None,\n",
        "  'pink block': None,\n",
        "  'cyan block': None,\n",
        "  'brown block': None,\n",
        "  'gray block': None,\n",
        "}\n",
        "\n",
        "COLORS = {\n",
        "  'blue':   (78/255,  121/255, 167/255, 255/255),\n",
        "  'red':    (255/255,  87/255,  89/255, 255/255),\n",
        "  'green':  (89/255,  169/255,  79/255, 255/255),\n",
        "  'orange': (242/255, 142/255,  43/255, 255/255),\n",
        "  'yellow': (237/255, 201/255,  72/255, 255/255),\n",
        "  'purple': (176/255, 122/255, 161/255, 255/255),\n",
        "  'pink':   (255/255, 157/255, 167/255, 255/255),\n",
        "  'cyan':   (118/255, 183/255, 178/255, 255/255),\n",
        "  'brown':  (156/255, 117/255,  95/255, 255/255),\n",
        "  'gray':   (186/255, 176/255, 172/255, 255/255),\n",
        "}\n",
        "\n",
        "PLACE_TARGETS = {\n",
        "  'blue block': None,\n",
        "  'red block': None,\n",
        "  'green block': None,\n",
        "  'orange block': None,\n",
        "  'yellow block': None,\n",
        "  'purple block': None,\n",
        "  'pink block': None,\n",
        "  'cyan block': None,\n",
        "  'brown block': None,\n",
        "  'gray block': None,\n",
        "\n",
        "  'blue bowl': None,\n",
        "  'red bowl': None,\n",
        "  'green bowl': None,\n",
        "  'orange bowl': None,\n",
        "  'yellow bowl': None,\n",
        "  'purple bowl': None,\n",
        "  'pink bowl': None,\n",
        "  'cyan bowl': None,\n",
        "  'brown bowl': None,\n",
        "  'gray bowl': None,\n",
        "\n",
        "  'top left corner':     (-0.3 + 0.05, -0.2 - 0.05, 0),\n",
        "  'top side':            (0,           -0.2 - 0.05, 0),\n",
        "  'top right corner':    (0.3 - 0.05,  -0.2 - 0.05, 0),\n",
        "  'left side':           (-0.3 + 0.05, -0.5,        0),\n",
        "  'middle':              (0,           -0.5,        0),\n",
        "  'right side':          (0.3 - 0.05,  -0.5,        0),\n",
        "  'bottom left corner':  (-0.3 + 0.05, -0.8 + 0.05, 0),\n",
        "  'bottom side':         (0,           -0.8 + 0.05, 0),\n",
        "  'bottom right corner': (0.3 - 0.05,  -0.8 + 0.05, 0),\n",
        "}\n",
        "\n",
        "PIXEL_SIZE = 0.00267857\n",
        "BOUNDS = np.float32([[-0.3, 0.3], [-0.8, -0.2], [0, 0.15]])  # (X, Y, Z)"
      ],
      "metadata": {
        "id": "D6gTAdeX39yd",
        "cellView": "form"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown **Gripper class:** adds a gripper to the robot and runs a parallel thread to simulate single-actuator behavior.\n",
        "\n",
        "class Robotiq2F85:\n",
        "  \"\"\"Gripper handling for Robotiq 2F85.\"\"\"\n",
        "\n",
        "  def __init__(self, robot, tool):\n",
        "    self.robot = robot\n",
        "    self.tool = tool\n",
        "    pos = [0.1339999999999999, -0.49199999999872496, 0.5]\n",
        "    rot = pybullet.getQuaternionFromEuler([np.pi, 0, np.pi])\n",
        "    urdf = 'robotiq_2f_85/robotiq_2f_85.urdf'\n",
        "    self.body = pybullet.loadURDF(urdf, pos, rot)\n",
        "    self.n_joints = pybullet.getNumJoints(self.body)\n",
        "    self.activated = False\n",
        "\n",
        "    # Connect gripper base to robot tool.\n",
        "    pybullet.createConstraint(self.robot, tool, self.body, 0, jointType=pybullet.JOINT_FIXED, jointAxis=[0, 0, 0], parentFramePosition=[0, 0, 0], childFramePosition=[0, 0, -0.07], childFrameOrientation=pybullet.getQuaternionFromEuler([0, 0, np.pi / 2]))\n",
        "\n",
        "    # Set friction coefficients for gripper fingers.\n",
        "    for i in range(pybullet.getNumJoints(self.body)):\n",
        "      pybullet.changeDynamics(self.body, i, lateralFriction=10.0, spinningFriction=1.0, rollingFriction=1.0, frictionAnchor=True)\n",
        "\n",
        "    # Start thread to handle additional gripper constraints.\n",
        "    self.motor_joint = 1\n",
        "    self.running = True\n",
        "    self.constraints_thread = threading.Thread(target=self.step)\n",
        "    self.constraints_thread.daemon = True\n",
        "    self.constraints_thread.start()\n",
        "\n",
        "  # Control joint positions by enforcing hard contraints on gripper behavior.\n",
        "  # Set one joint as the open/close motor joint (other joints should mimic).\n",
        "  def step(self):\n",
        "    while self.running:\n",
        "      try:\n",
        "        currj = [pybullet.getJointState(self.body, i)[0] for i in range(self.n_joints)]\n",
        "        indj = [6, 3, 8, 5, 10]\n",
        "        targj = [currj[1], -currj[1], -currj[1], currj[1], currj[1]]\n",
        "        pybullet.setJointMotorControlArray(self.body, indj, pybullet.POSITION_CONTROL, targj, positionGains=np.ones(5))\n",
        "      except:\n",
        "        return\n",
        "      time.sleep(0.001)\n",
        "\n",
        "  # Close gripper fingers.\n",
        "  def activate(self):\n",
        "    pybullet.setJointMotorControl2(self.body, self.motor_joint, pybullet.VELOCITY_CONTROL, targetVelocity=1, force=10)\n",
        "    self.activated = True\n",
        "\n",
        "  # Open gripper fingers.\n",
        "  def release(self):\n",
        "    pybullet.setJointMotorControl2(self.body, self.motor_joint, pybullet.VELOCITY_CONTROL, targetVelocity=-1, force=10)\n",
        "    self.activated = False\n",
        "\n",
        "  # If activated and object in gripper: check object contact.\n",
        "  # If activated and nothing in gripper: check gripper contact.\n",
        "  # If released: check proximity to surface (disabled).\n",
        "  def detect_contact(self):\n",
        "    obj, _, ray_frac = self.check_proximity()\n",
        "    if self.activated:\n",
        "      empty = self.grasp_width() \u003c 0.01\n",
        "      cbody = self.body if empty else obj\n",
        "      if obj == self.body or obj == 0:\n",
        "        return False\n",
        "      return self.external_contact(cbody)\n",
        "  #   else:\n",
        "  #     return ray_frac \u003c 0.14 or self.external_contact()\n",
        "\n",
        "  # Return if body is in contact with something other than gripper\n",
        "  def external_contact(self, body=None):\n",
        "    if body is None:\n",
        "      body = self.body\n",
        "    pts = pybullet.getContactPoints(bodyA=body)\n",
        "    pts = [pt for pt in pts if pt[2] != self.body]\n",
        "    return len(pts) \u003e 0  # pylint: disable=g-explicit-length-test\n",
        "\n",
        "  def check_grasp(self):\n",
        "    while self.moving():\n",
        "      time.sleep(0.001)\n",
        "    success = self.grasp_width() \u003e 0.01\n",
        "    return success\n",
        "\n",
        "  def grasp_width(self):\n",
        "    lpad = np.array(pybullet.getLinkState(self.body, 4)[0])\n",
        "    rpad = np.array(pybullet.getLinkState(self.body, 9)[0])\n",
        "    dist = np.linalg.norm(lpad - rpad) - 0.047813\n",
        "    return dist\n",
        "\n",
        "  def check_proximity(self):\n",
        "    ee_pos = np.array(pybullet.getLinkState(self.robot, self.tool)[0])\n",
        "    tool_pos = np.array(pybullet.getLinkState(self.body, 0)[0])\n",
        "    vec = (tool_pos - ee_pos) / np.linalg.norm((tool_pos - ee_pos))\n",
        "    ee_targ = ee_pos + vec\n",
        "    ray_data = pybullet.rayTest(ee_pos, ee_targ)[0]\n",
        "    obj, link, ray_frac = ray_data[0], ray_data[1], ray_data[2]\n",
        "    return obj, link, ray_frac"
      ],
      "metadata": {
        "id": "0bj91lBq6WeK",
        "cellView": "form"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown **Gym-style environment class:** this initializes a robot overlooking a workspace with objects.\n",
        "\n",
        "class PickPlaceEnv():\n",
        "\n",
        "  def __init__(self):\n",
        "    self.dt = 1/480\n",
        "    self.sim_step = 0\n",
        "\n",
        "    # Configure and start PyBullet.\n",
        "    # python3 -m pybullet_utils.runServer\n",
        "    # pybullet.connect(pybullet.SHARED_MEMORY)  # pybullet.GUI for local GUI.\n",
        "    pybullet.connect(pybullet.DIRECT)  # pybullet.GUI for local GUI.\n",
        "    pybullet.configureDebugVisualizer(pybullet.COV_ENABLE_GUI, 0)\n",
        "    pybullet.setPhysicsEngineParameter(enableFileCaching=0)\n",
        "    assets_path = os.path.dirname(os.path.abspath(\"\"))\n",
        "    pybullet.setAdditionalSearchPath(assets_path)\n",
        "    pybullet.setAdditionalSearchPath(pybullet_data.getDataPath())\n",
        "    pybullet.setTimeStep(self.dt)\n",
        "\n",
        "    self.home_joints = (np.pi / 2, -np.pi / 2, np.pi / 2, -np.pi / 2, 3 * np.pi / 2, 0)  # Joint angles: (J0, J1, J2, J3, J4, J5).\n",
        "    self.home_ee_euler = (np.pi, 0, np.pi)  # (RX, RY, RZ) rotation in Euler angles.\n",
        "    self.ee_link_id = 9  # Link ID of UR5 end effector.\n",
        "    self.tip_link_id = 10  # Link ID of gripper finger tips.\n",
        "    self.gripper = None\n",
        "\n",
        "  def reset(self, config):\n",
        "    pybullet.resetSimulation(pybullet.RESET_USE_DEFORMABLE_WORLD)\n",
        "    pybullet.setGravity(0, 0, -9.8)\n",
        "    self.cache_video = []\n",
        "\n",
        "    # Temporarily disable rendering to load URDFs faster.\n",
        "    pybullet.configureDebugVisualizer(pybullet.COV_ENABLE_RENDERING, 0)\n",
        "\n",
        "    # Add robot.\n",
        "    pybullet.loadURDF(\"plane.urdf\", [0, 0, -0.001])\n",
        "    self.robot_id = pybullet.loadURDF(\"ur5e/ur5e.urdf\", [0, 0, 0], flags=pybullet.URDF_USE_MATERIAL_COLORS_FROM_MTL)\n",
        "    self.ghost_id = pybullet.loadURDF(\"ur5e/ur5e.urdf\", [0, 0, -10])  # For forward kinematics.\n",
        "    self.joint_ids = [pybullet.getJointInfo(self.robot_id, i) for i in range(pybullet.getNumJoints(self.robot_id))]\n",
        "    self.joint_ids = [j[0] for j in self.joint_ids if j[2] == pybullet.JOINT_REVOLUTE]\n",
        "\n",
        "    # Move robot to home configuration.\n",
        "    for i in range(len(self.joint_ids)):\n",
        "      pybullet.resetJointState(self.robot_id, self.joint_ids[i], self.home_joints[i])\n",
        "\n",
        "    # Add gripper.\n",
        "    if self.gripper is not None:\n",
        "      while self.gripper.constraints_thread.is_alive():\n",
        "        self.constraints_thread_active = False\n",
        "    self.gripper = Robotiq2F85(self.robot_id, self.ee_link_id)\n",
        "    self.gripper.release()\n",
        "\n",
        "    # Add workspace.\n",
        "    plane_shape = pybullet.createCollisionShape(pybullet.GEOM_BOX, halfExtents=[0.3, 0.3, 0.001])\n",
        "    plane_visual = pybullet.createVisualShape(pybullet.GEOM_BOX, halfExtents=[0.3, 0.3, 0.001])\n",
        "    plane_id = pybullet.createMultiBody(0, plane_shape, plane_visual, basePosition=[0, -0.5, 0])\n",
        "    pybullet.changeVisualShape(plane_id, -1, rgbaColor=[0.2, 0.2, 0.2, 1.0])\n",
        "\n",
        "    # Load objects according to config.\n",
        "    self.config = config\n",
        "    self.obj_name_to_id = {}\n",
        "    obj_names = list(self.config['pick']) + list(self.config['place'])\n",
        "    obj_xyz = np.zeros((0, 3))\n",
        "    for obj_name in obj_names:\n",
        "      if ('block' in obj_name) or ('bowl' in obj_name):\n",
        "\n",
        "        # Get random position 15cm+ from other objects.\n",
        "        while True:\n",
        "          rand_x = np.random.uniform(BOUNDS[0, 0] + 0.1, BOUNDS[0, 1] - 0.1)\n",
        "          rand_y = np.random.uniform(BOUNDS[1, 0] + 0.1, BOUNDS[1, 1] - 0.1)\n",
        "          rand_xyz = np.float32([rand_x, rand_y, 0.03]).reshape(1, 3)\n",
        "          if len(obj_xyz) == 0:\n",
        "            obj_xyz = np.concatenate((obj_xyz, rand_xyz), axis=0)\n",
        "            break\n",
        "          else:\n",
        "            nn_dist = np.min(np.linalg.norm(obj_xyz - rand_xyz, axis=1)).squeeze()\n",
        "            if nn_dist \u003e 0.15:\n",
        "              obj_xyz = np.concatenate((obj_xyz, rand_xyz), axis=0)\n",
        "              break\n",
        "\n",
        "        object_color = COLORS[obj_name.split(' ')[0]]\n",
        "        object_type = obj_name.split(' ')[1]\n",
        "        object_position = rand_xyz.squeeze()\n",
        "        if object_type == 'block':\n",
        "          object_shape = pybullet.createCollisionShape(pybullet.GEOM_BOX, halfExtents=[0.02, 0.02, 0.02])\n",
        "          object_visual = pybullet.createVisualShape(pybullet.GEOM_BOX, halfExtents=[0.02, 0.02, 0.02])\n",
        "          object_id = pybullet.createMultiBody(0.01, object_shape, object_visual, basePosition=object_position)\n",
        "        elif object_type == 'bowl':\n",
        "          object_position[2] = 0\n",
        "          object_id = pybullet.loadURDF(\"bowl/bowl.urdf\", object_position, useFixedBase=1)\n",
        "        pybullet.changeVisualShape(object_id, -1, rgbaColor=object_color)\n",
        "        self.obj_name_to_id[obj_name] = object_id\n",
        "\n",
        "    # Re-enable rendering.\n",
        "    pybullet.configureDebugVisualizer(pybullet.COV_ENABLE_RENDERING, 1)\n",
        "\n",
        "    for _ in range(200):\n",
        "      pybullet.stepSimulation()\n",
        "    print('Environment reset: done.')\n",
        "    return self.get_observation()\n",
        "\n",
        "  def servoj(self, joints):\n",
        "    \"\"\"Move to target joint positions with position control.\"\"\"\n",
        "    pybullet.setJointMotorControlArray(\n",
        "      bodyIndex=self.robot_id,\n",
        "      jointIndices=self.joint_ids,\n",
        "      controlMode=pybullet.POSITION_CONTROL,\n",
        "      targetPositions=joints,\n",
        "      positionGains=[0.01]*6)\n",
        "\n",
        "  def movep(self, position):\n",
        "    \"\"\"Move to target end effector position.\"\"\"\n",
        "    joints = pybullet.calculateInverseKinematics(\n",
        "        bodyUniqueId=self.robot_id,\n",
        "        endEffectorLinkIndex=self.tip_link_id,\n",
        "        targetPosition=position,\n",
        "        targetOrientation=pybullet.getQuaternionFromEuler(self.home_ee_euler),\n",
        "        maxNumIterations=100)\n",
        "    self.servoj(joints)\n",
        "\n",
        "  def step(self, action=None):\n",
        "    \"\"\"Do pick and place motion primitive.\"\"\"\n",
        "    pick_xyz, place_xyz = action['pick'].copy(), action['place'].copy()\n",
        "\n",
        "    # Set fixed primitive z-heights.\n",
        "    hover_xyz = pick_xyz.copy() + np.float32([0, 0, 0.2])\n",
        "    pick_xyz[2] -= 0.02\n",
        "    pick_xyz[2] = max(pick_xyz[2], 0.03)\n",
        "    place_xyz[2] = 0.15\n",
        "\n",
        "    # Move to object.\n",
        "    ee_xyz = np.float32(pybullet.getLinkState(self.robot_id, self.tip_link_id)[0])\n",
        "    while np.linalg.norm(hover_xyz - ee_xyz) \u003e 0.01:\n",
        "      self.movep(hover_xyz)\n",
        "      self.step_sim_and_render()\n",
        "      ee_xyz = np.float32(pybullet.getLinkState(self.robot_id, self.tip_link_id)[0])\n",
        "    while np.linalg.norm(pick_xyz - ee_xyz) \u003e 0.01:\n",
        "      self.movep(pick_xyz)\n",
        "      self.step_sim_and_render()\n",
        "      ee_xyz = np.float32(pybullet.getLinkState(self.robot_id, self.tip_link_id)[0])\n",
        "\n",
        "    # Pick up object.\n",
        "    self.gripper.activate()\n",
        "    for _ in range(240):\n",
        "      self.step_sim_and_render()\n",
        "    while np.linalg.norm(hover_xyz - ee_xyz) \u003e 0.01:\n",
        "      self.movep(hover_xyz)\n",
        "      self.step_sim_and_render()\n",
        "      ee_xyz = np.float32(pybullet.getLinkState(self.robot_id, self.tip_link_id)[0])\n",
        "\n",
        "    # Move to place location.\n",
        "    while np.linalg.norm(place_xyz - ee_xyz) \u003e 0.01:\n",
        "      self.movep(place_xyz)\n",
        "      self.step_sim_and_render()\n",
        "      ee_xyz = np.float32(pybullet.getLinkState(self.robot_id, self.tip_link_id)[0])\n",
        "\n",
        "    # Place down object.\n",
        "    while (not self.gripper.detect_contact()) and (place_xyz[2] \u003e 0.03):\n",
        "      place_xyz[2] -= 0.001\n",
        "      self.movep(place_xyz)\n",
        "      for _ in range(3):\n",
        "        self.step_sim_and_render()\n",
        "    self.gripper.release()\n",
        "    for _ in range(240):\n",
        "      self.step_sim_and_render()\n",
        "    place_xyz[2] = 0.2\n",
        "    ee_xyz = np.float32(pybullet.getLinkState(self.robot_id, self.tip_link_id)[0])\n",
        "    while np.linalg.norm(place_xyz - ee_xyz) \u003e 0.01:\n",
        "      self.movep(place_xyz)\n",
        "      self.step_sim_and_render()\n",
        "      ee_xyz = np.float32(pybullet.getLinkState(self.robot_id, self.tip_link_id)[0])\n",
        "    place_xyz = np.float32([0, -0.5, 0.2])\n",
        "    while np.linalg.norm(place_xyz - ee_xyz) \u003e 0.01:\n",
        "      self.movep(place_xyz)\n",
        "      self.step_sim_and_render()\n",
        "      ee_xyz = np.float32(pybullet.getLinkState(self.robot_id, self.tip_link_id)[0])\n",
        "\n",
        "    observation = self.get_observation()\n",
        "    reward = self.get_reward()\n",
        "    done = False\n",
        "    info = {}\n",
        "    return observation, reward, done, info\n",
        "\n",
        "  def step_sim_and_render(self):\n",
        "    pybullet.stepSimulation()\n",
        "    self.sim_step += 1\n",
        "\n",
        "    # Render current image at 8 FPS.\n",
        "    if self.sim_step % (1 / (8 * self.dt)) == 0:\n",
        "      self.cache_video.append(self.get_camera_image())\n",
        "\n",
        "  def get_camera_image(self):\n",
        "    image_size = (240, 240)\n",
        "    intrinsics = (120., 0, 120., 0, 120., 120., 0, 0, 1)\n",
        "    color, _, _, _, _ = env.render_image(image_size, intrinsics)\n",
        "    return color\n",
        "\n",
        "  def set_alpha_transparency(self, alpha: float) -\u003e None:\n",
        "    for id in range(20):\n",
        "      visual_shape_data = pybullet.getVisualShapeData(id)\n",
        "      for i in range(len(visual_shape_data)):\n",
        "        object_id, link_index, _, _, _, _, _, rgba_color = visual_shape_data[i]\n",
        "        rgba_color = list(rgba_color[0:3]) +  [alpha]\n",
        "        pybullet.changeVisualShape(self.robot_id, linkIndex=i, rgbaColor=rgba_color)\n",
        "        pybullet.changeVisualShape(self.gripper.body, linkIndex=i, rgbaColor=rgba_color)\n",
        "\n",
        "  def get_camera_image_top(self,\n",
        "                           image_size=(240, 240),\n",
        "                           intrinsics=(2000., 0, 2000., 0, 2000., 2000., 0, 0, 1),\n",
        "                           position=(0, -0.5, 5),\n",
        "                           orientation=(0, np.pi, -np.pi / 2),\n",
        "                           zrange=(0.01, 1.),\n",
        "                           set_alpha=True):\n",
        "    set_alpha and self.set_alpha_transparency(0)\n",
        "    color, _, _, _, _ = env.render_image_top(image_size,\n",
        "                                             intrinsics,\n",
        "                                             position,\n",
        "                                             orientation,\n",
        "                                             zrange)\n",
        "    set_alpha and self.set_alpha_transparency(1)\n",
        "    return color\n",
        "\n",
        "  def render_image_top(self,\n",
        "                       image_size=(240, 240),\n",
        "                       intrinsics=(2000., 0, 2000., 0, 2000., 2000., 0, 0, 1),\n",
        "                       position=(0, -0.5, 5),\n",
        "                       orientation=(0, np.pi, -np.pi / 2),\n",
        "                       zrange=(0.01, 1.)):\n",
        "\n",
        "    # Camera parameters.\n",
        "    orientation = pybullet.getQuaternionFromEuler(orientation)\n",
        "    noise=True\n",
        "\n",
        "    # OpenGL camera settings.\n",
        "    lookdir = np.float32([0, 0, 1]).reshape(3, 1)\n",
        "    updir = np.float32([0, -1, 0]).reshape(3, 1)\n",
        "    rotation = pybullet.getMatrixFromQuaternion(orientation)\n",
        "    rotm = np.float32(rotation).reshape(3, 3)\n",
        "    lookdir = (rotm @ lookdir).reshape(-1)\n",
        "    updir = (rotm @ updir).reshape(-1)\n",
        "    lookat = position + lookdir\n",
        "    focal_len = intrinsics[0]\n",
        "    znear, zfar = (0.01, 10.)\n",
        "    viewm = pybullet.computeViewMatrix(position, lookat, updir)\n",
        "    fovh = (image_size[0] / 2) / focal_len\n",
        "    fovh = 180 * np.arctan(fovh) * 2 / np.pi\n",
        "\n",
        "    # Notes: 1) FOV is vertical FOV 2) aspect must be float\n",
        "    aspect_ratio = image_size[1] / image_size[0]\n",
        "    projm = pybullet.computeProjectionMatrixFOV(fovh, aspect_ratio, znear, zfar)\n",
        "\n",
        "    # Render with OpenGL camera settings.\n",
        "    _, _, color, depth, segm = pybullet.getCameraImage(\n",
        "        width=image_size[1],\n",
        "        height=image_size[0],\n",
        "        viewMatrix=viewm,\n",
        "        projectionMatrix=projm,\n",
        "        shadow=1,\n",
        "        flags=pybullet.ER_SEGMENTATION_MASK_OBJECT_AND_LINKINDEX,\n",
        "        renderer=pybullet.ER_BULLET_HARDWARE_OPENGL)\n",
        "\n",
        "    # Get color image.\n",
        "    color_image_size = (image_size[0], image_size[1], 4)\n",
        "    color = np.array(color, dtype=np.uint8).reshape(color_image_size)\n",
        "    color = color[:, :, :3]  # remove alpha channel\n",
        "    if noise:\n",
        "      color = np.int32(color)\n",
        "      color += np.int32(np.random.normal(0, 3, color.shape))\n",
        "      color = np.uint8(np.clip(color, 0, 255))\n",
        "\n",
        "    # Get depth image.\n",
        "    depth_image_size = (image_size[0], image_size[1])\n",
        "    zbuffer = np.float32(depth).reshape(depth_image_size)\n",
        "    depth = (zfar + znear - (2 * zbuffer - 1) * (zfar - znear))\n",
        "    depth = (2 * znear * zfar) / depth\n",
        "    if noise:\n",
        "      depth += np.random.normal(0, 0.003, depth.shape)\n",
        "\n",
        "    intrinsics = np.float32(intrinsics).reshape(3, 3)\n",
        "    return color, depth, position, orientation, intrinsics\n",
        "\n",
        "  def get_reward(self):\n",
        "    return 0  # TODO: check did the robot follow text instructions?\n",
        "\n",
        "  def get_observation(self):\n",
        "    observation = {}\n",
        "\n",
        "    # Render current image.\n",
        "    color, depth, position, orientation, intrinsics = self.render_image()\n",
        "\n",
        "    # Get heightmaps and colormaps.\n",
        "    points = self.get_pointcloud(depth, intrinsics)\n",
        "    position = np.float32(position).reshape(3, 1)\n",
        "    rotation = pybullet.getMatrixFromQuaternion(orientation)\n",
        "    rotation = np.float32(rotation).reshape(3, 3)\n",
        "    transform = np.eye(4)\n",
        "    transform[:3, :] = np.hstack((rotation, position))\n",
        "    points = self.transform_pointcloud(points, transform)\n",
        "    heightmap, colormap, xyzmap = self.get_heightmap(points, color, BOUNDS, PIXEL_SIZE)\n",
        "\n",
        "    observation[\"image\"] = colormap\n",
        "    observation[\"xyzmap\"] = xyzmap\n",
        "    return observation\n",
        "\n",
        "  def render_image(self, image_size=(720, 720), intrinsics=(360., 0, 360., 0, 360., 360., 0, 0, 1)):\n",
        "\n",
        "    # Camera parameters.\n",
        "    position = (0, -0.85, 0.4)\n",
        "    orientation = (np.pi / 4 + np.pi / 48, np.pi, np.pi)\n",
        "    orientation = pybullet.getQuaternionFromEuler(orientation)\n",
        "    zrange = (0.01, 10.)\n",
        "    noise=True\n",
        "\n",
        "    # OpenGL camera settings.\n",
        "    lookdir = np.float32([0, 0, 1]).reshape(3, 1)\n",
        "    updir = np.float32([0, -1, 0]).reshape(3, 1)\n",
        "    rotation = pybullet.getMatrixFromQuaternion(orientation)\n",
        "    rotm = np.float32(rotation).reshape(3, 3)\n",
        "    lookdir = (rotm @ lookdir).reshape(-1)\n",
        "    updir = (rotm @ updir).reshape(-1)\n",
        "    lookat = position + lookdir\n",
        "    focal_len = intrinsics[0]\n",
        "    znear, zfar = (0.01, 10.)\n",
        "    viewm = pybullet.computeViewMatrix(position, lookat, updir)\n",
        "    fovh = (image_size[0] / 2) / focal_len\n",
        "    fovh = 180 * np.arctan(fovh) * 2 / np.pi\n",
        "\n",
        "    # Notes: 1) FOV is vertical FOV 2) aspect must be float\n",
        "    aspect_ratio = image_size[1] / image_size[0]\n",
        "    projm = pybullet.computeProjectionMatrixFOV(fovh, aspect_ratio, znear, zfar)\n",
        "\n",
        "    # Render with OpenGL camera settings.\n",
        "    _, _, color, depth, segm = pybullet.getCameraImage(\n",
        "        width=image_size[1],\n",
        "        height=image_size[0],\n",
        "        viewMatrix=viewm,\n",
        "        projectionMatrix=projm,\n",
        "        shadow=1,\n",
        "        flags=pybullet.ER_SEGMENTATION_MASK_OBJECT_AND_LINKINDEX,\n",
        "        renderer=pybullet.ER_BULLET_HARDWARE_OPENGL)\n",
        "\n",
        "    # Get color image.\n",
        "    color_image_size = (image_size[0], image_size[1], 4)\n",
        "    color = np.array(color, dtype=np.uint8).reshape(color_image_size)\n",
        "    color = color[:, :, :3]  # remove alpha channel\n",
        "    if noise:\n",
        "      color = np.int32(color)\n",
        "      color += np.int32(np.random.normal(0, 3, color.shape))\n",
        "      color = np.uint8(np.clip(color, 0, 255))\n",
        "\n",
        "    # Get depth image.\n",
        "    depth_image_size = (image_size[0], image_size[1])\n",
        "    zbuffer = np.float32(depth).reshape(depth_image_size)\n",
        "    depth = (zfar + znear - (2 * zbuffer - 1) * (zfar - znear))\n",
        "    depth = (2 * znear * zfar) / depth\n",
        "    if noise:\n",
        "      depth += np.random.normal(0, 0.003, depth.shape)\n",
        "\n",
        "    intrinsics = np.float32(intrinsics).reshape(3, 3)\n",
        "    return color, depth, position, orientation, intrinsics\n",
        "\n",
        "  def get_pointcloud(self, depth, intrinsics):\n",
        "    \"\"\"Get 3D pointcloud from perspective depth image.\n",
        "    Args:\n",
        "      depth: HxW float array of perspective depth in meters.\n",
        "      intrinsics: 3x3 float array of camera intrinsics matrix.\n",
        "    Returns:\n",
        "      points: HxWx3 float array of 3D points in camera coordinates.\n",
        "    \"\"\"\n",
        "    height, width = depth.shape\n",
        "    xlin = np.linspace(0, width - 1, width)\n",
        "    ylin = np.linspace(0, height - 1, height)\n",
        "    px, py = np.meshgrid(xlin, ylin)\n",
        "    px = (px - intrinsics[0, 2]) * (depth / intrinsics[0, 0])\n",
        "    py = (py - intrinsics[1, 2]) * (depth / intrinsics[1, 1])\n",
        "    points = np.float32([px, py, depth]).transpose(1, 2, 0)\n",
        "    return points\n",
        "\n",
        "  def transform_pointcloud(self, points, transform):\n",
        "    \"\"\"Apply rigid transformation to 3D pointcloud.\n",
        "    Args:\n",
        "      points: HxWx3 float array of 3D points in camera coordinates.\n",
        "      transform: 4x4 float array representing a rigid transformation matrix.\n",
        "    Returns:\n",
        "      points: HxWx3 float array of transformed 3D points.\n",
        "    \"\"\"\n",
        "    padding = ((0, 0), (0, 0), (0, 1))\n",
        "    homogen_points = np.pad(points.copy(), padding,\n",
        "                            'constant', constant_values=1)\n",
        "    for i in range(3):\n",
        "      points[Ellipsis, i] = np.sum(transform[i, :] * homogen_points, axis=-1)\n",
        "    return points\n",
        "\n",
        "  def get_heightmap(self, points, colors, bounds, pixel_size):\n",
        "    \"\"\"Get top-down (z-axis) orthographic heightmap image from 3D pointcloud.\n",
        "    Args:\n",
        "      points: HxWx3 float array of 3D points in world coordinates.\n",
        "      colors: HxWx3 uint8 array of values in range 0-255 aligned with points.\n",
        "      bounds: 3x2 float array of values (rows: X,Y,Z; columns: min,max) defining\n",
        "        region in 3D space to generate heightmap in world coordinates.\n",
        "      pixel_size: float defining size of each pixel in meters.\n",
        "    Returns:\n",
        "      heightmap: HxW float array of height (from lower z-bound) in meters.\n",
        "      colormap: HxWx3 uint8 array of backprojected color aligned with heightmap.\n",
        "      xyzmap: HxWx3 float array of XYZ points in world coordinates.\n",
        "    \"\"\"\n",
        "    width = int(np.round((bounds[0, 1] - bounds[0, 0]) / pixel_size))\n",
        "    height = int(np.round((bounds[1, 1] - bounds[1, 0]) / pixel_size))\n",
        "    heightmap = np.zeros((height, width), dtype=np.float32)\n",
        "    colormap = np.zeros((height, width, colors.shape[-1]), dtype=np.uint8)\n",
        "    xyzmap = np.zeros((height, width, 3), dtype=np.float32)\n",
        "\n",
        "    # Filter out 3D points that are outside of the predefined bounds.\n",
        "    ix = (points[Ellipsis, 0] \u003e= bounds[0, 0]) \u0026 (points[Ellipsis, 0] \u003c bounds[0, 1])\n",
        "    iy = (points[Ellipsis, 1] \u003e= bounds[1, 0]) \u0026 (points[Ellipsis, 1] \u003c bounds[1, 1])\n",
        "    iz = (points[Ellipsis, 2] \u003e= bounds[2, 0]) \u0026 (points[Ellipsis, 2] \u003c bounds[2, 1])\n",
        "    valid = ix \u0026 iy \u0026 iz\n",
        "    points = points[valid]\n",
        "    colors = colors[valid]\n",
        "\n",
        "    # Sort 3D points by z-value, which works with array assignment to simulate\n",
        "    # z-buffering for rendering the heightmap image.\n",
        "    iz = np.argsort(points[:, -1])\n",
        "    points, colors = points[iz], colors[iz]\n",
        "    px = np.int32(np.floor((points[:, 0] - bounds[0, 0]) / pixel_size))\n",
        "    py = np.int32(np.floor((points[:, 1] - bounds[1, 0]) / pixel_size))\n",
        "    px = np.clip(px, 0, width - 1)\n",
        "    py = np.clip(py, 0, height - 1)\n",
        "    heightmap[py, px] = points[:, 2] - bounds[2, 0]\n",
        "    for c in range(colors.shape[-1]):\n",
        "      colormap[py, px, c] = colors[:, c]\n",
        "      xyzmap[py, px, c] = points[:, c]\n",
        "    colormap = colormap[::-1, :, :]  # Flip up-down.\n",
        "    xv, yv = np.meshgrid(np.linspace(BOUNDS[0, 0], BOUNDS[0, 1], height),\n",
        "                         np.linspace(BOUNDS[1, 0], BOUNDS[1, 1], width))\n",
        "    xyzmap[:, :, 0] = xv\n",
        "    xyzmap[:, :, 1] = yv\n",
        "    xyzmap = xyzmap[::-1, :, :]  # Flip up-down.\n",
        "    heightmap = heightmap[::-1, :]  # Flip up-down.\n",
        "    return heightmap, colormap, xyzmap\n",
        "\n",
        "def xyz_to_pix(position):\n",
        "  \"\"\"Convert from 3D position to pixel location on heightmap.\"\"\"\n",
        "  u = int(np.round((BOUNDS[1, 1] - position[1]) / PIXEL_SIZE))\n",
        "  v = int(np.round((position[0] - BOUNDS[0, 0]) / PIXEL_SIZE))\n",
        "  return (u, v)"
      ],
      "metadata": {
        "id": "ELFr8iE28hVX",
        "cellView": "form"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Initialize environment and render images.\n",
        "\n",
        "if 'env' in locals():\n",
        "  # Safely exit gripper threading before re-initializing environment.\n",
        "  env.gripper.running = False\n",
        "  while env.gripper.constraints_thread.isAlive():\n",
        "    time.sleep(0.01)\n",
        "env = PickPlaceEnv()\n",
        "\n",
        "# Define and reset environment.\n",
        "config = {'pick':  ['yellow block', 'green block', 'blue block'],\n",
        "          'place': ['yellow bowl', 'green bowl', 'blue bowl']}\n",
        "\n",
        "np.random.seed(42)\n",
        "obs = env.reset(config)\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "img = env.get_camera_image()\n",
        "plt.title('Perspective side-view')\n",
        "plt.imshow(img)\n",
        "plt.subplot(1, 2, 2)\n",
        "img = env.get_camera_image_top()\n",
        "img = np.flipud(img.transpose(1, 0, 2))\n",
        "plt.title('Orthographic top-view')\n",
        "plt.imshow(img)\n",
        "plt.show()\n",
        "\n",
        "# Note: orthographic cameras do not exist. But we can approximate them by\n",
        "# projecting a 3D point cloud from an RGB-D camera, then unprojecting that onto\n",
        "# an orthographic plane. Orthographic views are useful for spatial action maps.\n",
        "plt.title('Unprojected orthographic top-view')\n",
        "plt.imshow(obs['image'])\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "HclXxMiNFHyE",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Scenario distribution**\n",
        "Here we define the distribution of possible scenarios. For example, a scenario include:\n",
        "* Environment: three blocks and bowls of color blue, green, and yellow on the table\n",
        "* User instruction (possibly ambiguous): \"put the block in the green bowl\"\n",
        "* Goal: put the blue block in the green bowl\n",
        "\n",
        "We will use a relatively simple setup here --- please refer to Appendix A6 in the paper for the full-scale distribution used in experiments.\n",
        "\n",
        "* Environment distribution: the same set of three blocks and bowls of color blue, green, and yellow on the table\n",
        "* User instruction distribution: uniform over\n",
        "  * \"put the block in the {color} bowl\" (ambiguous in block color)\n",
        "  * \"put the {color} block in the bowl\" (ambiguous in bowl color)\n",
        "  * \"put the {color} block in the {color} bowl\" (unambiguous)\n",
        "  * \"put the {color} block close to the {color} bowl\" (ambiguous in spatial relation)\n",
        "  * \"put the {color} block to the {direction} of the {color} bowl\" (unambiguous).\n",
        "* Goal: uniform over the possible ambiguities\n",
        "\u003c!-- (e.g., \"put the block in the {color} bowl\", and the goal distribution is uniform over the three colors). --\u003e"
      ],
      "metadata": {
        "id": "ggv1sy8IZOQC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown **Set up distribution**\n",
        "\n",
        "blocks = ['green block', 'blue block', 'yellow block']\n",
        "bowls = ['green bowl', 'blue bowl', 'yellow bowl']\n",
        "\n",
        "instruction_ambiguities = {\n",
        "    'put the block in the {color} bowl': 'block color',\n",
        "    'put the {color} block in the bowl': 'bowl color',\n",
        "    'put the {color} block in the {color} bowl': None,\n",
        "    'put the {color} block close to the {color} bowl': 'direction',\n",
        "    'put the {color} block to the {direction} of the {color} bowl': None\n",
        "}\n",
        "instruction_templates = list(instruction_ambiguities.keys())\n",
        "\n",
        "colors = ['green', 'blue', 'yellow']\n",
        "directions = ['front', 'back', 'left', 'right']"
      ],
      "metadata": {
        "id": "hOUvcAd4aNrV"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown **Specify number of calibration data**: with more calibration data, the guarantee from conformal prediction would be tighter and precise. We recommend at least 200 scenarios (400 used in the paper), but you can use a small number first to save the number of API calls.\n",
        "\n",
        "num_calibration = 100  #@param {type: \"number\"} {form-width: \"10%\"}"
      ],
      "metadata": {
        "cellView": "form",
        "id": "OxVjuyW-0895"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown **Sample from the distribution**\n",
        "\n",
        "dataset = []\n",
        "for i in range(num_calibration):\n",
        "  data = {}\n",
        "  instruction_orig = random.choice(instruction_templates)\n",
        "  instruction = instruction_orig\n",
        "\n",
        "  # sample colors if needed\n",
        "  num_color_in_instruction = instruction.count('{color}')\n",
        "  if num_color_in_instruction \u003e 0:\n",
        "    color_instruction = random.choices(colors, k=num_color_in_instruction)\n",
        "    for color in color_instruction:\n",
        "      instruction = instruction.replace('{color}', color)\n",
        "\n",
        "  # sample didrection if needed\n",
        "  if '{direction}' in instruction:\n",
        "    direction = random.choice(directions)\n",
        "    instruction = instruction.replace('{direction}', direction)\n",
        "\n",
        "  # sample goal based on ambiguities\n",
        "  ambiguity = instruction_ambiguities[instruction_orig]\n",
        "  if ambiguity and 'color' in ambiguity:\n",
        "    true_color = random.choice(colors)\n",
        "  elif ambiguity and 'direction' in ambiguity:\n",
        "    true_direction = random.choice(directions)\n",
        "\n",
        "  # determine the goal in the format of [pick_obj, relation (in, left, right, front, back), target_obj]\n",
        "  instruction_split = instruction.split()\n",
        "  block_attr = instruction_split[instruction_split.index('block')-1]\n",
        "  if 'the' == block_attr: # ambiguous\n",
        "    pick_obj = true_color + ' block'\n",
        "  else:\n",
        "    pick_obj = block_attr + ' block'\n",
        "  bowl_attr = instruction_split[instruction_split.index('bowl')-1]\n",
        "  if 'the' == bowl_attr: # ambiguous\n",
        "    target_obj = true_color + ' bowl'\n",
        "  else:\n",
        "    target_obj = bowl_attr + ' bowl'\n",
        "  if 'close to' in instruction:\n",
        "    relation = true_direction\n",
        "  elif 'in' in instruction:\n",
        "    relation = 'in'\n",
        "  else:\n",
        "    relation = instruction_split[instruction_split.index('of')-1] # bit hacky\n",
        "\n",
        "  # fill in data\n",
        "  data['environment'] = blocks + bowls # fixed set\n",
        "  data['instruction'] = instruction\n",
        "  data['goal'] = [pick_obj, relation, target_obj]\n",
        "  dataset.append(data)\n",
        "\n",
        "# print a few\n",
        "print('Showing the first five sampled scenarios')\n",
        "for i in range(5):\n",
        "  data = dataset[i]\n",
        "  print(f'==== {i} ====')\n",
        "  print('Instruction:', data['instruction'])\n",
        "  print('Goal (pick_obj, relation, target_obj):', data['goal'])"
      ],
      "metadata": {
        "id": "VxqQXLUJlHBw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "69ea2c28-2e22-48f3-984d-258cc92010e5"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Showing the first five sampled scenarios\n",
            "==== 0 ====\n",
            "Instruction: put the yellow block to the front of the yellow bowl\n",
            "Goal (pick_obj, relation, target_obj): ['yellow block', 'front', 'yellow bowl']\n",
            "==== 1 ====\n",
            "Instruction: put the block in the green bowl\n",
            "Goal (pick_obj, relation, target_obj): ['green block', 'in', 'green bowl']\n",
            "==== 2 ====\n",
            "Instruction: put the green block to the left of the green bowl\n",
            "Goal (pick_obj, relation, target_obj): ['green block', 'left', 'green bowl']\n",
            "==== 3 ====\n",
            "Instruction: put the green block in the green bowl\n",
            "Goal (pick_obj, relation, target_obj): ['green block', 'in', 'green bowl']\n",
            "==== 4 ====\n",
            "Instruction: put the yellow block in the bowl\n",
            "Goal (pick_obj, relation, target_obj): ['yellow block', 'in', 'yellow bowl']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "## **LLM Calibration with Conformal Prediction**\n",
        "Given the calibration dataset, now we calibrate the LLM with conformal prediction. After calibration, KnowNo can generate a prediction set that includes the correct action in an ambiguous scenario with a user-specified probability! For example:\n",
        "\n",
        "* Instruction: \"put the block in the green bowl\"\n",
        "* Goal: put the blue block in the green bowl\n",
        "* Prediction set: [\"put the green block in the green bowl\", \"put the blue block in the green bowl\", \"put the yellow block in the green bowl\"]\n"
      ],
      "metadata": {
        "id": "P8cLiNt7sBEq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "## **Multiple Choice Question Answering**\n",
        "For calibration, we need to access the uncertainty of the LLM given the user instruction and the environment description. In KnowNo, we formulate the LLM planning as Multiple Choice Question Answering (MCQA): for each scenario, KnowNo first applies few-shot prompting to generate plausible options to take, and then queries the likelihood of predicting the choice token (A, B, C, D, E). These likelihood values reveal the LLM uncertainty."
      ],
      "metadata": {
        "id": "ch1EIbE_r5Bl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown **Prompt LLM to generate multiple choices**\n",
        "\n",
        "# prompt for MC generation\n",
        "mc_gen_prompt_template = \"\"\"\n",
        "We: You are a robot, and you are asked to move objects to precise locations on the table. Our instructions can be ambiguous.\n",
        "\n",
        "We: On the table there are these objects: blue block, yellow bowl, yellow block, green bowl, green block, blue bowl.\n",
        "We: Now, put the grass-colored bowl at the right side of the blue round object\n",
        "You: These are some options:\n",
        "A) put blue bowl at the right side of blue block\n",
        "B) put green bowl at the right side of blue bowl\n",
        "C) put green block at the right side of blue bowl\n",
        "D) put yellow bowl at the right side of blue bowl\n",
        "\n",
        "We: On the table there are these objects: yellow bowl, green bowl, green block, yellow block, blue block, blue bowl.\n",
        "We: Now, put the yellow square object near the green box\n",
        "You: These are some options:\n",
        "A) put yellow block in front of green block\n",
        "B) put yellow block behind green block\n",
        "C) put yellow block to the left of green block\n",
        "D) put yellow block to the right of green block\n",
        "\n",
        "We: On the table there are these objects: blue bowl, yellow block, green bowl, blue block, green block, yellow bowl.\n",
        "We: Now, put the yellow bowl along the horizontal axis of the grass-colored block\n",
        "You: These are some options:\n",
        "A) put yellow bowl at the front of the green block\n",
        "B) put yellow bowl at the left side of the green block\n",
        "C) put yellow bowl at the left side of the blue block\n",
        "D) put yellow bowl at the right side of the green block\n",
        "\n",
        "We: On the table there are these objects: green bowl, yellow block, blue bowl, yellow bowl, green block, blue block.\n",
        "We: Now, {instruction}\n",
        "You: These are some options:\n",
        "\"\"\"\n",
        "\n",
        "for i in tqdm.trange(len(dataset)):\n",
        "  data = dataset[i]\n",
        "  instruction = data['instruction']\n",
        "  mc_gen_prompt = mc_gen_prompt_template.replace('{instruction}', instruction).strip()\n",
        "  if i == 0:\n",
        "    print('===== Prompt for generating multiple choices for the first scenario =====')\n",
        "    print(mc_gen_prompt)\n",
        "\n",
        "  # call LLM API\n",
        "  _, mc_gen_output = lm(mc_gen_prompt, logit_bias={})\n",
        "  if i == 0:\n",
        "    print('\\n===== Result =====')\n",
        "    print(mc_gen_output)\n",
        "  data['mc_gen_output'] = mc_gen_output\n",
        "  dataset[i] = data"
      ],
      "metadata": {
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              "  0%|          | 0/100 [00:00\u003c?, ?it/s]"
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          "text": [
            "===== Prompt for generating multiple choices for the first scenario =====\n",
            "We: You are a robot, and you are asked to move objects to precise locations on the table. Our instructions can be ambiguous.\n",
            "\n",
            "We: On the table there are these objects: blue block, yellow bowl, yellow block, green bowl, green block, blue bowl.\n",
            "We: Now, put the grass-colored bowl at the right side of the blue round object\n",
            "You: These are some options:\n",
            "A) put blue bowl at the right side of blue block\n",
            "B) put green bowl at the right side of blue bowl\n",
            "C) put green block at the right side of blue bowl\n",
            "D) put yellow bowl at the right side of blue bowl\n",
            "\n",
            "We: On the table there are these objects: yellow bowl, green bowl, green block, yellow block, blue block, blue bowl.\n",
            "We: Now, put the yellow square object near the green box\n",
            "You: These are some options:\n",
            "A) put yellow block in front of green block\n",
            "B) put yellow block behind green block\n",
            "C) put yellow block to the left of green block\n",
            "D) put yellow block to the right of green block\n",
            "\n",
            "We: On the table there are these objects: blue bowl, yellow block, green bowl, blue block, green block, yellow bowl.\n",
            "We: Now, put the yellow bowl along the horizontal axis of the grass-colored block\n",
            "You: These are some options:\n",
            "A) put yellow bowl at the front of the green block\n",
            "B) put yellow bowl at the left side of the green block\n",
            "C) put yellow bowl at the left side of the blue block\n",
            "D) put yellow bowl at the right side of the green block\n",
            "\n",
            "We: On the table there are these objects: green bowl, yellow block, blue bowl, yellow bowl, green block, blue block.\n",
            "We: Now, put the yellow block to the front of the yellow bowl\n",
            "You: These are some options:\n",
            "\n",
            "===== Result =====\n",
            "A) put yellow block in front of yellow bowl\n",
            "B) put yellow block behind yellow bowl\n",
            "C) put yellow block to the left of yellow bowl\n",
            "D) put yellow block to the right of yellow bowl\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown **Post-process the multiple choices and prompt LLM to predict**\n",
        "\n",
        "# heuristics for extracting the multiple choices\n",
        "def process_mc_raw(mc_raw, add_mc='an option not listed here'):\n",
        "  mc_processed_all = []\n",
        "  mc_all = mc_raw.split('\\n')\n",
        "  for mc in mc_all:\n",
        "      mc = mc.strip()\n",
        "\n",
        "      # skip nonsense\n",
        "      if len(mc) \u003c 5 or mc[0] not in [\n",
        "          'a', 'b', 'c', 'd', 'A', 'B', 'C', 'D', '1', '2', '3', '4'\n",
        "      ]:\n",
        "          continue\n",
        "\n",
        "      mc = mc[2:]  # remove a), b), ...\n",
        "      mc = mc.strip().lower().split('.')[0]\n",
        "      mc_processed_all.append(mc)\n",
        "  if len(mc_processed_all) \u003c 4:\n",
        "      raise 'Cannot extract four options from the raw output.'\n",
        "\n",
        "  # Check if any repeated option - use do nothing as substitute\n",
        "  mc_processed_all = list(set(mc_processed_all))\n",
        "  if len(mc_processed_all) \u003c 4:\n",
        "      num_need = 4 - len(mc_processed_all)\n",
        "      for _ in range(num_need):\n",
        "          mc_processed_all.append('do nothing')\n",
        "  prefix_all = ['A) ', 'B) ', 'C) ', 'D) ']\n",
        "  if add_mc is not None:\n",
        "      mc_processed_all.append(add_mc)\n",
        "      prefix_all.append('E) ')\n",
        "  random.shuffle(mc_processed_all)\n",
        "\n",
        "  # combines the multiple choices into a single string\n",
        "  mc_prompt = ''\n",
        "  for mc_ind, (prefix, mc) in enumerate(zip(prefix_all, mc_processed_all)):\n",
        "      mc_prompt += prefix + mc\n",
        "      if mc_ind \u003c len(mc_processed_all) - 1:\n",
        "          mc_prompt += '\\n'\n",
        "  add_mc_prefix = prefix_all[mc_processed_all.index(add_mc)][0]\n",
        "  return mc_prompt, mc_processed_all, add_mc_prefix\n",
        "\n",
        "mc_score_prompt_template = \"\"\"\n",
        "We: You are a robot, and you are asked to move objects to precise locations on the table. Our instructions can be ambiguous.\n",
        "\n",
        "We: On the table there are these objects: green bowl, yellow block, blue bowl, yellow bowl, green block, blue block.\n",
        "We: Now, {instruction}\n",
        "You: These are some options:\n",
        "{mc}\n",
        "We: Which option is correct? Answer with a single letter.\n",
        "You:\n",
        "\"\"\"\n",
        "\n",
        "for i in tqdm.trange(len(dataset)):\n",
        "  data = dataset[i]\n",
        "  instruction = data['instruction']\n",
        "  mc_gen_output = data['mc_gen_output']\n",
        "\n",
        "  # process multiple choices\n",
        "  mc_gen_full, mc_gen_all, add_mc_prefix = process_mc_raw(mc_gen_output)\n",
        "\n",
        "  # get new prompt\n",
        "  mc_score_prompt = mc_score_prompt_template.replace('{instruction}', instruction).replace('{mc}', mc_gen_full).strip()\n",
        "  data['mc_score_prompt'] = mc_score_prompt\n",
        "  if i == 0:\n",
        "    print('==== Prompt for LLM predicting next-token ====')\n",
        "    print(mc_score_prompt)\n",
        "\n",
        "  # call LLM API\n",
        "  mc_score_response, _ = lm(mc_score_prompt, max_tokens=1, logprobs=5)\n",
        "  top_logprobs_full = mc_score_response[\"choices\"][0][\"logprobs\"][\"top_logprobs\"][0]\n",
        "  top_tokens = [token.strip() for token in top_logprobs_full.keys()]\n",
        "  top_logprobs = [value for value in top_logprobs_full.values()]\n",
        "  if i == 0:\n",
        "    print('\\n===== Result =====')\n",
        "    print('Tokens:', top_tokens)\n",
        "    print('Log probabilities:', top_logprobs)\n",
        "\n",
        "  # save\n",
        "  data['mc_gen_full'] = mc_gen_full\n",
        "  data['mc_gen_all'] = mc_gen_all\n",
        "  data['add_mc_prefix'] = add_mc_prefix\n",
        "  data['mc_score_response'] = mc_score_response\n",
        "  data['top_logprobs_full'] = top_logprobs_full\n",
        "  data['top_tokens'] = top_tokens\n",
        "  data['top_logprobs'] = top_logprobs\n",
        "  dataset[i] = data"
      ],
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          "text": [
            "==== Prompt for LLM predicting next-token ====\n",
            "We: You are a robot, and you are asked to move objects to precise locations on the table. Our instructions can be ambiguous.\n",
            "\n",
            "We: On the table there are these objects: green bowl, yellow block, blue bowl, yellow bowl, green block, blue block.\n",
            "We: Now, put the yellow block to the front of the yellow bowl\n",
            "You: These are some options:\n",
            "A) put yellow block in front of yellow bowl\n",
            "B) put yellow block to the right of yellow bowl\n",
            "C) an option not listed here\n",
            "D) put yellow block to the left of yellow bowl\n",
            "E) put yellow block behind yellow bowl\n",
            "We: Which option is correct? Answer with a single letter.\n",
            "You:\n",
            "\n",
            "===== Result =====\n",
            "Tokens: ['D', 'A', 'B', 'C', 'E']\n",
            "Log probabilities: [-0.022618081, -3.8068893, -8.940358, -11.283467, -12.583501]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You may notice how sharp the probability distribution is. This is the result of applying RLHF in LLM training that kills the entropy. The beauty of conformal prediction is to generate set-based predictions that come with statistical coverage guarantee even with such miscalibrated probabilities!"
      ],
      "metadata": {
        "id": "5_0LYOcO-PGp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Specify Target Success Level**"
      ],
      "metadata": {
        "id": "g8Fvgp_2-fR5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown This is $1-\\epsilon$ in the paper, the probability that the prediction set contains the true option at test time.\n",
        "target_success = 0.8  #@param {type: \"number\"} {form-width: \"10%\"}\n",
        "epsilon = 1-target_success\n",
        "print('Epsilon:', epsilon)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "NLzB0zcn7CMc",
        "outputId": "aa863e59-1a42-4293-b05d-f7baf9b39e1b"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epsilon: 0.19999999999999996\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Determine Score Threshold**\n",
        "We now perform calibration based on the next-token prediction results from the calibration set and the target error level $ϵ$. The result is a threshold $1-\\widehat{q}$ that at test time, if the prediction set includes all options with likelihood higher than $1-\\widehat{q}$, then the set is guaranteed to include the **true** option with $1-\\epsilon$ probability."
      ],
      "metadata": {
        "id": "96KoIcdB70K5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown First, we use simple heuristics to determine the true labels, i.e., the correct option from the multiple choices.\n",
        "for i, data in enumerate(dataset):\n",
        "  mc_gen_all = data['mc_gen_all']\n",
        "  goal = data['goal']\n",
        "\n",
        "  # go through all mc\n",
        "  token_all = ['A', 'B', 'C', 'D', 'E']\n",
        "  true_options = []\n",
        "  for mc_ind, mc in enumerate(mc_gen_all):\n",
        "    if 'not listed here' in mc or 'do nothing' in mc: continue\n",
        "    if 'block ' not in mc: continue  # no object to be picked\n",
        "\n",
        "    # extract pick_obj from mc\n",
        "    mc_split = mc.split()\n",
        "    mc_pick_obj_attr = mc_split[mc_split.index('block')-1]\n",
        "    mc_pick_obj = mc_pick_obj_attr + ' block'\n",
        "\n",
        "    # extract target_obj from mc\n",
        "    try: # try bowl first\n",
        "      mc_target_obj_attr = mc_split[mc_split.index('bowl')-1]\n",
        "    except:\n",
        "      mc_target_obj_attr = mc_split[mc_split.index('block', -1)-1]\n",
        "    mc_target_obj = mc_target_obj_attr + ' bowl'\n",
        "\n",
        "    # extract spatial relation from mc\n",
        "    if 'left' in mc:\n",
        "      relation = 'left'\n",
        "    elif 'right' in mc:\n",
        "      relation = 'right'\n",
        "    elif 'front' in mc:\n",
        "      relation = 'front'\n",
        "    elif 'back' in mc or 'behind' in mc:\n",
        "      relation = 'back'\n",
        "    else:\n",
        "      relation = 'in'\n",
        "\n",
        "    # check with goal\n",
        "    if mc_pick_obj == goal[0] and relation == goal[1] and mc_target_obj == goal[2]:\n",
        "      true_options.append(token_all[mc_ind])\n",
        "\n",
        "  # if none correct\n",
        "  if len(true_options) == 0:\n",
        "    true_options = [data['add_mc_prefix']]\n",
        "\n",
        "  # save\n",
        "  dataset[i]['true_options'] = true_options\n",
        "\n",
        "  if i == 2:\n",
        "    print('==== Sample scenario ====')\n",
        "    print(data['mc_score_prompt'])\n",
        "    print('True options:', true_options)\n",
        "    print('probs:', data['top_tokens'], data['top_logprobs'])"
      ],
      "metadata": {
        "cellView": "form",
        "id": "loDZWupd8c7c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08236e01-fabc-4a6b-da84-472e38fc6953"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==== Sample scenario ====\n",
            "We: You are a robot, and you are asked to move objects to precise locations on the table. Our instructions can be ambiguous.\n",
            "\n",
            "We: On the table there are these objects: green bowl, yellow block, blue bowl, yellow bowl, green block, blue block.\n",
            "We: Now, put the green block to the left of the green bowl\n",
            "You: These are some options:\n",
            "A) an option not listed here\n",
            "B) put green block at the right side of the green bowl\n",
            "C) put green block at the left side of the green bowl\n",
            "D) put green block at the back of the green bowl\n",
            "E) put green block at the front of the green bowl\n",
            "We: Which option is correct? Answer with a single letter.\n",
            "You:\n",
            "True options: ['C']\n",
            "probs: ['C', 'B', 'A', 'D', 'E'] [-3.655073e-05, -10.541807, -11.589895, -14.323225, -16.59844]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Then, get the non-conformity scores from the calibration set, which is 1 minus the likelihood of the **true** option, $1-f(x)_{y_\\text{true}}$.\n",
        "def temperature_scaling(logits, temperature):\n",
        "    logits = np.array(logits)\n",
        "    logits /= temperature\n",
        "\n",
        "    # apply softmax\n",
        "    logits -= logits.max()\n",
        "    logits = logits - np.log(np.sum(np.exp(logits)))\n",
        "    smx = np.exp(logits)\n",
        "    return smx\n",
        "\n",
        "non_conformity_score = []\n",
        "for data in dataset:\n",
        "  top_logprobs = data['top_logprobs']\n",
        "  top_tokens = data['top_tokens']\n",
        "  true_options = data['true_options']\n",
        "\n",
        "  # normalize the five scores to sum of 1\n",
        "  mc_smx_all = temperature_scaling(top_logprobs, temperature=5)\n",
        "\n",
        "  # get the softmax value of true option\n",
        "  true_label_smx = [mc_smx_all[token_ind]\n",
        "                    for token_ind, token in enumerate(top_tokens)\n",
        "                    if token in true_options]\n",
        "  true_label_smx = np.max(true_label_smx)\n",
        "\n",
        "  # get non-comformity score\n",
        "  non_conformity_score.append(1 - true_label_smx)"
      ],
      "metadata": {
        "cellView": "form",
        "id": "FilkFkgvBFQu"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Find $\\widehat{q}$ as the $\\frac{\\lceil (N+1)(1-\\epsilon) \\rceil}{N}$ quantile of the non-conformity scores, where $N$ is number of calibration data. Also plot the histogram of the scores and the quantile.\n",
        "q_level = np.ceil((num_calibration + 1) * (1 - epsilon)) / num_calibration\n",
        "qhat = np.quantile(non_conformity_score, q_level, method='higher')\n",
        "print('Quantile value qhat:', qhat)\n",
        "print('')\n",
        "\n",
        "# plot histogram and quantile\n",
        "plt.figure(figsize=(6, 2))\n",
        "plt.hist(non_conformity_score, bins=30, edgecolor='k', linewidth=1)\n",
        "plt.axvline(\n",
        "    x=qhat, linestyle='--', color='r', label='Quantile value'\n",
        ")\n",
        "plt.title(\n",
        "    'Histogram of non-comformity scores in the calibration set'\n",
        ")\n",
        "plt.xlabel('Non-comformity score')\n",
        "plt.legend(); plt.show()\n",
        "print('')\n",
        "print('A good predictor should have low non-comformity scores, concentrated at the left side of the figure')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "cellView": "form",
        "id": "DqEqNDdRBnnM",
        "outputId": "bbec83f5-9989-48ae-918e-2386c7dc62fa"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Quantile value qhat: 0.8865697621883813\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u003cFigure size 600x200 with 1 Axes\u003e"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "A good predictor should have low non-comformity scores, concentrated at the left side of the figure\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You may see there is a number of data with large non-comformity scores, which means the prediction is badly wrong. This is due to the inherent bias in the GPT3.5 model such as biasing \"left\" over other directions when the direction is under-specified.\n",
        "\n",
        "We also applied temperature scaling=5 to smooth the probability distribution a bit.\n"
      ],
      "metadata": {
        "id": "AP9_qzJ7G5pT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Specify your own instruction**\n",
        "You can specify your own instruction here. If you did not run calibration above, you can choose `skip_calibration` here and applies calibration result based on target success level=0.8. Running the block will show the prediction set!"
      ],
      "metadata": {
        "id": "kIFMX1cAFVEg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "instruction = \"put the yellow block next to the green bowl.\" #@param {type:\"string\"}\n",
        "skip_calibration = False #@param {type:\"boolean\"}\n",
        "verbose = False #@param {type:\"boolean\"}\n",
        "if skip_calibration: qhat = 0.927 # based on epsilon=0.2\n",
        "\n",
        "import random\n",
        "def process_mc_raw(mc_raw, add_mc='an option not listed here'):\n",
        "  mc_processed_all = []\n",
        "  mc_all = mc_raw.split('\\n')\n",
        "  for mc in mc_all:\n",
        "      mc = mc.strip()\n",
        "\n",
        "      # skip nonsense\n",
        "      if len(mc) \u003c 5 or mc[0] not in [\n",
        "          'a', 'b', 'c', 'd', 'A', 'B', 'C', 'D', '1', '2', '3', '4'\n",
        "      ]:\n",
        "          continue\n",
        "\n",
        "      mc = mc[2:]  # remove a), b), ...\n",
        "      mc = mc.strip().lower().split('.')[0]\n",
        "      mc_processed_all.append(mc)\n",
        "  if len(mc_processed_all) \u003c 4:\n",
        "      raise 'Cannot extract four options from the raw output.'\n",
        "\n",
        "  # Check if any repeated option - use do nothing as substitute\n",
        "  mc_processed_all = list(set(mc_processed_all))\n",
        "  if len(mc_processed_all) \u003c 4:\n",
        "      num_need = 4 - len(mc_processed_all)\n",
        "      for _ in range(num_need):\n",
        "          mc_processed_all.append('do nothing')\n",
        "  prefix_all = ['A) ', 'B) ', 'C) ', 'D) ']\n",
        "  if add_mc is not None:\n",
        "      mc_processed_all.append(add_mc)\n",
        "      prefix_all.append('E) ')\n",
        "  random.shuffle(mc_processed_all)\n",
        "\n",
        "  # combines the multiple choices into a single string\n",
        "  mc_prompt = ''\n",
        "  for mc_ind, (prefix, mc) in enumerate(zip(prefix_all, mc_processed_all)):\n",
        "      mc_prompt += prefix + mc\n",
        "      if mc_ind \u003c len(mc_processed_all) - 1:\n",
        "          mc_prompt += '\\n'\n",
        "  add_mc_prefix = prefix_all[mc_processed_all.index(add_mc)][0]\n",
        "  return mc_prompt, mc_processed_all, add_mc_prefix\n",
        "\n",
        "def temperature_scaling(logits, temperature):\n",
        "    logits = np.array(logits)\n",
        "    logits /= temperature\n",
        "\n",
        "    # apply softmax\n",
        "    logits -= logits.max()\n",
        "    logits = logits - np.log(np.sum(np.exp(logits)))\n",
        "    smx = np.exp(logits)\n",
        "    return smx\n",
        "\n",
        "demo_mc_gen_prompt_template = \"\"\"\n",
        "We: You are a robot, and you are asked to move objects to precise locations on the table. Our instructions can be ambiguous.\n",
        "\n",
        "We: On the table there are these objects: blue block, yellow bowl, yellow block, green bowl, green block, blue bowl.\n",
        "We: Now, put the grass-colored bowl at the right side of the blue round object\n",
        "You: These are some options:\n",
        "A) put blue bowl at the right side of blue block\n",
        "B) put green bowl at the right side of blue bowl\n",
        "C) put green block at the right side of blue bowl\n",
        "D) put yellow bowl at the right side of blue bowl\n",
        "\n",
        "We: On the table there are these objects: yellow bowl, green bowl, green block, yellow block, blue block, blue bowl.\n",
        "We: Now, put the yellow square object near the green box\n",
        "You: These are some options:\n",
        "A) put yellow block in front of green block\n",
        "B) put yellow block behind green block\n",
        "C) put yellow block to the left of green block\n",
        "D) put yellow block to the right of green block\n",
        "\n",
        "We: On the table there are these objects: blue bowl, yellow block, green bowl, blue block, green block, yellow bowl.\n",
        "We: Now, put the yellow bowl along the horizontal axis of the grass-colored block\n",
        "You: These are some options:\n",
        "A) put yellow bowl at the front of the green block\n",
        "B) put yellow bowl at the left side of the green block\n",
        "C) put yellow bowl at the left side of the blue block\n",
        "D) put yellow bowl at the right side of the green block\n",
        "\n",
        "We: On the table there are these objects: green bowl, yellow block, blue bowl, yellow bowl, green block, blue block.\n",
        "We: Now, {instruction}\n",
        "You: These are some options:\n",
        "\"\"\"\n",
        "\n",
        "demo_mc_score_prompt_template = \"\"\"\n",
        "We: You are a robot, and you are asked to move objects to precise locations on the table. Our instructions can be ambiguous.\n",
        "\n",
        "We: On the table there are these objects: green bowl, yellow block, blue bowl, yellow bowl, green block, blue block.\n",
        "We: Now, {instruction}\n",
        "You: These are some options:\n",
        "{mc}\n",
        "We: Which option is correct? Answer with a single letter.\n",
        "You:\n",
        "\"\"\"\n",
        "\n",
        "# Get prompt for generating multiple choices\n",
        "demo_mc_gen_prompt = demo_mc_gen_prompt_template.replace('{instruction}', instruction).strip()\n",
        "if verbose:\n",
        "  print('===== Prompt for generating multiple choices =====')\n",
        "  print(demo_mc_gen_prompt)\n",
        "\n",
        "# Generate multiple choices\n",
        "_, demo_mc_gen_output = lm(demo_mc_gen_prompt, logit_bias={})\n",
        "if verbose:\n",
        "  print('\\n===== Result =====')\n",
        "  print(demo_mc_gen_output)\n",
        "demo_mc_gen_full, demo_mc_gen_all, add_mc_prefix = process_mc_raw(demo_mc_gen_output)\n",
        "\n",
        "# get new prompt\n",
        "demo_mc_score_prompt = demo_mc_score_prompt_template.replace('{instruction}', instruction).replace('{mc}', demo_mc_gen_full).strip()\n",
        "if verbose:\n",
        "  print('\\n==== Prompt for LLM predicting next-token ====')\n",
        "  print(demo_mc_score_prompt)\n",
        "\n",
        "# call LLM API\n",
        "demo_mc_score_response, _ = lm(demo_mc_score_prompt, max_tokens=1, logprobs=5)\n",
        "top_logprobs_full = demo_mc_score_response[\"choices\"][0][\"logprobs\"][\"top_logprobs\"][0]\n",
        "top_tokens = [token.strip() for token in top_logprobs_full.keys()]\n",
        "top_logprobs = [value for value in top_logprobs_full.values()]\n",
        "\n",
        "# get the softmax value of true option\n",
        "mc_smx_all = temperature_scaling(top_logprobs, temperature=5)\n",
        "\n",
        "true_label_smx = [mc_smx_all[token_ind]\n",
        "                  for token_ind, token in enumerate(top_tokens)\n",
        "                  if token in true_options]\n",
        "true_label_smx = np.max(true_label_smx)\n",
        "\n",
        "# get non-comformity score\n",
        "non_conformity_score.append(1 - true_label_smx)\n",
        "\n",
        "# include all options with score \u003e= 1-qhat\n",
        "prediction_set = [\n",
        "          token for token_ind, token in enumerate(top_tokens)\n",
        "          if mc_smx_all[token_ind] \u003e= 1 - qhat\n",
        "      ]\n",
        "\n",
        "# print\n",
        "print('\\n===== Multiple choices generated =====')\n",
        "print(demo_mc_gen_full)\n",
        "print('\\n===== Prediction set =====')\n",
        "print(prediction_set)\n",
        "if len(prediction_set) == 1:\n",
        "  print('Singleton set. No help needed!')\n",
        "else:\n",
        "  print('Set with multiple options. Help needed!')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "mMlAjjXJFeOY",
        "outputId": "2ab9aaa0-af75-4791-94ee-66aab1c80b28"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "===== Multiple choices generated =====\n",
            "A) put yellow block to the left of green bowl\n",
            "B) put yellow block in front of green bowl\n",
            "C) put yellow block behind green bowl\n",
            "D) put yellow block to the right of green bowl\n",
            "E) an option not listed here\n",
            "\n",
            "===== Prediction set =====\n",
            "['A', 'D', 'B']\n",
            "Set with multiple options. Help needed!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown If the prediction set has more than one option, human help is triggered. In that case, put the true option as a single capital letter here.\n",
        "user_chosen_option = 'D' #@param {type:\"string\"}\n",
        "tokens = ['A', 'B', 'C', 'D', 'E']\n",
        "action_option = demo_mc_gen_all[tokens.index(user_chosen_option)]\n",
        "\n",
        "if len(prediction_set) \u003e 1:\n",
        "  print('Option chosen by user:', action_option)\n",
        "else:\n",
        "  print('Option from prediction set without user intervention', )\n",
        "\n",
        "# extract pick and place locations\n",
        "if 'not listed here' in action_option or 'do nothing' in action_option or 'block ' not in action_option:\n",
        "  print('Invalid option! Cannot execute.')\n",
        "else:\n",
        "  # extract pick_obj from mc\n",
        "  action_option_split = action_option.split()\n",
        "  pick_obj_attr = action_option_split[action_option_split.index('block')-1]\n",
        "  pick_obj = pick_obj_attr + ' block'\n",
        "\n",
        "  # extract target_obj from mc\n",
        "  target_obj_attr = action_option_split[action_option_split.index('bowl')-1]\n",
        "  target_obj = target_obj_attr + ' bowl'\n",
        "\n",
        "  # extract spatial relation from mc\n",
        "  if 'left' in action_option:\n",
        "    relation = 'left'\n",
        "  elif 'right' in action_option:\n",
        "    relation = 'right'\n",
        "  elif 'front' in action_option:\n",
        "    relation = 'front'\n",
        "  elif 'back' in action_option or 'behind' in action_option:\n",
        "    relation = 'back'\n",
        "  else:\n",
        "    relation = 'in'\n",
        "  print('\\nPick object:', pick_obj)\n",
        "  print('Target object:', target_obj)\n",
        "  print('Spatial relation to target object:', relation)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "3SLh9sP2KF8P",
        "outputId": "ecc157d3-ff1b-49df-e174-e7b33ab89f39"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Option chosen by user: put yellow block to the right of green bowl\n",
            "\n",
            "Pick object: yellow block\n",
            "Target object: green bowl\n",
            "Spatial relation to target object: right\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Set up scripted pick and place oracle policy\n",
        "class ScriptedPolicy():\n",
        "\n",
        "  def __init__(self, env):\n",
        "    self.env = env\n",
        "\n",
        "  def get_action(self, pick_target, place_target, relation):\n",
        "\n",
        "    pick_id = self.env.obj_name_to_id[pick_target]\n",
        "    pick_pose = pybullet.getBasePositionAndOrientation(pick_id)\n",
        "    pick_position = np.float32(pick_pose[0])\n",
        "\n",
        "    if place_target in self.env.obj_name_to_id:\n",
        "      place_id = self.env.obj_name_to_id[place_target]\n",
        "      place_pose = pybullet.getBasePositionAndOrientation(place_id)\n",
        "      place_position = np.float32(place_pose[0])\n",
        "    else:\n",
        "      place_position = np.float32(PLACE_TARGETS[place_target])\n",
        "\n",
        "    # Add offset for relation\n",
        "    if relation == 'left':\n",
        "      place_position[0] -= 0.10\n",
        "    elif relation == 'right':\n",
        "      place_position[0] += 0.10\n",
        "    elif relation == 'front':\n",
        "      place_position[1] -= 0.10\n",
        "    elif relation == 'back':\n",
        "      place_position[1] += 0.10\n",
        "\n",
        "    act = {'pick': pick_position, 'place': place_position}\n",
        "    return act"
      ],
      "metadata": {
        "id": "QYwloVzrHBbE",
        "cellView": "form"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Reset environment\n",
        "config = {'pick':  ['yellow block', 'green block', 'blue block'],\n",
        "          'place': ['yellow bowl', 'green bowl', 'blue bowl']}\n",
        "\n",
        "np.random.seed(42)\n",
        "obs = env.reset(config)\n",
        "img = env.get_camera_image()\n",
        "fig = plt.figure(figsize=(3, 3))\n",
        "plt.imshow(img)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "JBZORSgnywxn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "outputId": "023f8046-90d5-4d9b-e845-16425d7c5263",
        "cellView": "form"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Environment reset: done.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u003cFigure size 300x300 with 1 Axes\u003e"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Run action!\n",
        "policy = ScriptedPolicy(env)\n",
        "act = policy.get_action(pick_obj, target_obj, relation)\n",
        "print(act)\n",
        "\n",
        "# Step environment.\n",
        "# act = {'pick': pick_xyz, 'place': place_xyz}\n",
        "before = env.get_camera_image()\n",
        "prev_obs = obs['image'].copy()\n",
        "obs, _, _, _ = env.step(act)\n",
        "\n",
        "# Show video of environment rollout.\n",
        "debug_clip = ImageSequenceClip(env.cache_video, fps=25)\n",
        "display(debug_clip.ipython_display(autoplay=1, loop=1, center=False))\n",
        "env.cache_video = []\n",
        "\n",
        "# Show camera image after pick and place.\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title('Before')\n",
        "plt.imshow(before)\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title('After')\n",
        "after = env.get_camera_image()\n",
        "plt.imshow(after)\n",
        "plt.show()"
      ],
      "metadata": {
        "cellView": "form",
        "id": "eEuNP0_JKHnH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "outputId": "9a9ea42e-13c9-4719-a610-f8fa29a692d1"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'pick': array([-0.05018499, -0.31971017,  0.02098936], dtype=float32), 'place': array([-0.0033031 , -0.49009743,  0.02      ], dtype=float32)}\n",
            "Moviepy - Building video __temp__.mp4.\n",
            "Moviepy - Writing video __temp__.mp4\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "                                                   "
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Moviepy - Done !\n",
            "Moviepy - video ready __temp__.mp4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u003cmoviepy.video.io.html_tools.HTML2 object\u003e"
            ],
            "text/html": [
              "\u003cvideo autoplay='1' 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controls\u003eSorry, seems like your browser doesn't support HTML5 audio/video\u003c/video\u003e"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u003cFigure size 640x480 with 2 Axes\u003e"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}
